Traffic sign recognition is an important topic in driver assistant system and intelligent autonomous vehicles. Traffic sign detection is a critical step, whose performance greatly affect the performance and computation cost of traffic sign recognition. In this paper, we propose a traffic sign detection method based on a scoring SVM model. First, traffic sign color and color gradient are extracted according to their color characteristics. Then, the shape of traffic sign is computed by a voting scheme, yielding shape score maps. After that, the score maps of traffic signs are used to train a SVM model. Compared with single traffic sign score value, the scoremapis more efficient to verify the existence of a traffic sign. Finally, the trained SVM model is used to detect traffic signs. Experiments show that the proposed method is more effective than the voting based method to detect traffic signs.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Robust traffic sign detection in complex road environments


    Contributors:
    Tian, Bin (author) / Chen, Ran (author) / Yao, Yanjie (author) / Li, Naiqiang (author)


    Publication date :

    2016-07-01


    Size :

    654205 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Traffic Sign Perception in Road Environments

    De la Escalera, A. / Moreno, L. / Armingol, J. M. et al. | British Library Conference Proceedings | 1994


    Road traffic sign detection and classification

    Escalera, A. de la / Moreno, L.E. / Salichs, M.A. et al. | Tema Archive | 1997


    Traffic Road Sign Detection and Classification

    Mehdi Fartaj / Sedigheh Ghofrani | DOAJ | 2024

    Free access

    Fast and robust traffic sign detection

    Soetedjo, A. / Yamada, K. | Tema Archive | 2005


    Mask R-CNN for Robust and Accurate Traffic Sign Detection in Dynamic Environments

    Rohini, P. / Harsh, V. / Lakshmi, P. Sree | Springer Verlag | 2025